Microclsutering using VISION
#Use the non-anchored data
DefaultAssay(BAL_list) <- "RNA"
#Initialize the microcluster data frame
pools.df <- data.frame(cluster = 1, cells = 1)
patients <- c(unique(BAL_list@meta.data$pat))
#Run the microclustering for each patient separately
for(i in 1:length(patients)){
BAL_list_subset <- subset(BAL_list, cells = c(row.names(BAL_list@meta.data[BAL_list@meta.data$pat == patients[i],])))
scaled.df <- BAL_list_subset@assays$RNA@data
scaled.df <- (2^(scaled.df) )-1
#Define Pools
pools <- applyMicroClustering(as.matrix(scaled.df), cellsPerPartition = round(ncol(scaled.df)*0.05), filterThreshold = 3)
assign(paste0(patients[i], "_pools"), pools)
# Create pooled versions of expression matrix
pooledExpression <- poolMatrixCols(scaled.df, pools)
colnames(pooledExpression) <- paste(patients[i], colnames(pooledExpression), sep="_")
assign(paste0(patients[i], "_pooledExpression"), pooledExpression)
#Create overview of pooled cells
for(j in 1:length(pools)){
tmp <- data.frame(cluster = rep(names(pools)[j], length(as.character(unlist(pools[j])))), cells = as.character(unlist(pools[j])))
tmp$cluster <- paste(patients[i], tmp$cluster, sep="_")
pools.df <<- rbind(pools.df, tmp)
}
}
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 12956
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1142
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 2685 cells into 21 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 14397
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1602
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 4555 cells into 22 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 14668
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1340
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 4420 cells into 22 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 17327
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1621
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 12085 cells into 17 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 17597
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1574
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 16190 cells into 22 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 14868
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1457
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 3060 cells into 22 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 12964
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1223
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 2640 cells into 20 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 14811
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1681
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 4343 cells into 20 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 12881
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1226
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 3399 cells into 24 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 14174
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1399
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 1524 cells into 19 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 14598
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1652
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 2566 cells into 21 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 11604
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1039
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 1414 cells into 20 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 12198
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1010
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 1788 cells into 21 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 11849
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1258
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 1108 cells into 21 pools
## Computing a latent space for microclustering using PCA...
## Determining lateng space genes...
## Applying Threshold filter...removing genes detected in less than 3 cells
## Genes Retained: 12120
## Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
## Genes Retained: 1302
## Performing PCA...
## Performing initial coarse-clustering...
## Further partitioning coarse clusters...
## Micro-pooling completed reducing 1344 cells into 21 pools
pools.df <- pools.df[-1,]